Universal Early Cancer Diagnostic

Description

The methylation of DNA is an important epigenetic modification that regulates gene expression. A variety of disease states, cancer in particular, are thought to originate from defects in this process leading to changes in the DNA methylation pattern across the genome. What governs this transition is unclear and therefore, understanding how the global DNA methylation landscape shifts during oncogenesis will allow for the early prediction and diagnosis of cancer.

Using whole genome sequencing and RNA sequencing, Professors Alex Meissner and Franziska Michor of the Harvard Stem Cell Institute identify genomic hotspots for DNA methylation during early embryonic development and the combination of epigenetic cofactors required to drive these changes. They discovered that the methylation patterns of placental progenitors during development and of cells during the onset of oncogenesis share similar regulatory pathways, and thus share molecular targets for therapeutic intervention. Further, these methylation signatures can be used to predict the onset of cancer and its tissue of origin in asymptomatic patients allowing earlier and more precise diagnoses.

Advantages of this technology over other competing approaches.

Universal: Even if different tissues express different oncogenes, all malignancies have aberrant changes in their methylation signatures.Unappreciated new molecular targets: Identification of a novel combination epigenetic cofactors that drive transition to the cancerous state allows the targeting of cancer-specific interactionsNon-invasive: Detection of methylation signatures uses cell-free DNA in the blood and does not require tissue biopsy.Earlier diagnosis: Because DNA methylation can occur prior to gene expression, methylation signatures can be used to predict the onset of cancer in asymptomatic individuals.Precise and personalized: The methylation landscape is specific to the tissue of origin and our database of dysregulated developmental gene promoters can predict these origins to allow for more precise diagnosis.